Internet of Things (IoT) is a network of uniquely-identifiable, purposed “things” that are enabled to communicate data pertaining thereto, there between over a communications network whereby, the communicated data form a basis for manipulating the operation of the “things”. The “thing” in the “Internet of Things” could virtually be anything that fits into a common purpose thereof. For example, the “thing” could be a person with a heart rate monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert its driver when tire pressure is low and the like. The “thing” can be any other natural or man-made entity that can be assigned with a unique IP address and provided with an ability to transfer data over a network. Notably, if all the entities in an IoT are machines, then the IoT is referred to as “a Machine to Machine” (M2M) IoT or simply, as M2M IoT.
It is apparent from the aforementioned examples that an entity becomes the “thing” of a M2M IoT especially, when the entity is attached with one or more sensors capable of capturing one or more types of data pertaining thereto: segregating the data (if applicable); selectively communicating each segregation of data to one or more fellow “things”; receiving one or more control commands (or instructions) from one or more fellow “things” wherein, the control commands are based on the data received by the fellow “things”; and executing one or more commands resulting in the manipulation or “management” of an operation of the corresponding entity. Therefore, in an IoT-enabled system, the “things” basically manage themselves without any human intervention, thus drastically improving the efficiency thereof.
US Patent application 20120330614 A1 discusses a method for obtaining information associated with a machine having one or more components, wherein the information includes multiple rules associated with the components. The method also includes receiving measurements of a vibration level of the machine and generating, based on the measurements, one or more feature values for one or more features associated with the one or more components. The method further includes determining a component-related condition for the one or more components based on the one or more feature values and rules. In addition, the method includes providing an indicator identifying the component-related condition. The application discusses a rule based system and mechanism. Further, the application fails to disclose an ability to adapt and learn.
U.S. Pat. No. 8,868,242 B2 discusses a system for monitoring a plant equipment. Another aspect provides an automated analysis system wherein software instructions operably compare sensor data to predefined valves and determine mechanical problems in multiple machines. In another aspect, a cement manufacturing system includes sensors for sensing movement conditions of cement making machines. A further aspect provides a central computer connected to vibration sensors associated with cement making machines where software instructions perform real-time comparisons and machine performance determinations based at least in part on sensed signals. The patent discusses manufacturing equipment and more particularly an automated analysis system for monitoring manufacturing plant machinery.
U.S. Pat. No. 8,920,078 B2 discusses a pneumatic conveyance of grain or other dry and loose commodities. The system conveys the commodity from a first piece of equipment to a second piece of equipment. Parameters used in determining blower motor speed may include the gauge pressure measured in a transport pipe, the rate of discharge of commodity into the transport pipe and the blower motor current or power. The patent fails to show or suggest a means of predictive maintenance. Further, it does not involve machine learning mechanisms.
US patent application 2014/0223767 A1 discusses a system for effectively purging heat regenerating desiccant compressed air dryers from a moisture load by a ‘tuned’ regenerating means with the use of a variable restriction on the blower purge air flow, a blower back-pressure set point, a heater discharge temperature set point and a bottom area bed temperature of a vessel containing desiccant, a means to balance purge temperature to a stable, non-cycling state preventing an under and overheating heating of desiccant, vessel and associated piping. A valve control means to terminate heating, cooling and dry purge operations selectively operated to allow purge flows to quickly cause water vapor captivated by the desiccant (in a drying cycle) to be released and purged out of the vessel (in a regeneration cycle) resulting in a regenerated tower vessel prepared for a compressed air drying cycle. However, the patent application fails to show any means of adapting and learning.
U.S. Pat. No. 5,610,339A discusses a method for determining and predicting a present and a future condition of a machine having at least one rotative element. The method includes an initial step of storing in a memory, a predetermined logic routine and at least one predetermined key frequency of the rotative element of the machine. The machine is operated in a predetermined operational state and during operation the mechanical motion of the rotative element is sensed by at least one sensor. The sensed mechanical motion of the rotative element is converted into a corresponding electrical signal and the predetermined operational state of the machine is correlated with the corresponding electrical signal. The corresponding electrical signal is inputted into corresponding vibration data by utilizing the predetermined logic routine. Then, the corresponding vibration data is compared with at least one predetermined key frequency to predict the present and future condition of the machine. A control signal is transmitted to the machine in order to control. This patent amongst others fails to show any means of collecting real time data from across location and also fails to adapt or learn.
It is evident from the discussion of the aforementioned prior arts that none of them pave way for the predictive and preventive maintenance of dryers through machine learning. The prior art inventions also fail to disclose the use of IoT. There is a need in the art for a solution to the aforementioned problem.